Method and apparatus for adaptive control of motor, and storage medium

ABSTRACT

A method for adaptive motor control includes acquiring current parameters in an operation process of the motor at a current moment; determining a type of a region in which the motor operates at the current moment according to the current parameters; triggering a corresponding motor model according to the type of the region in which the motor operates at the current moment; and inputting the current parameters into the corresponding motor model, generating control parameters for motor operation according to the current parameters, and controlling the operation of the motor according to the control parameters for motor operation. An apparatus and a computer-readable storage medium are also disclosed. In comparison with the conventional motor control which uses the single nonlinear model, the motor control method disclosed herein can greatly improve the reliability of the control.

TECHNICAL FIELD

The present disclosure relates to the field of motor control, and inparticular to a method and an apparatus for adaptive control of a motor,and to a storage medium.

BACKGROUND

Conventionally, if a motor is to be controlled to operate perfectly, anaccurate motor model needs to be known. Because the physical model ofexisting motors cannot completely adopt the widely used linear model, anonlinear model is generally used which approximates the physical modelof the motor. However, there are many problems in the construction andimplementation of the nonlinear model. For example, in constructing themodel of the motor, it can be quite difficult to ensure the consistencybetween the physical model and constructed nonlinear model of the motor,which may lead to a poor reliability of the nonlinear model; secondly,in measuring parameters of the motor, it can be difficult to measurenonlinear parameters, and the measured values are only approximatevalues. Therefore, when a mathematical model is used for expressing thereal motor, it certainly approximates the real motor, and the onlydifference lies in how close it approximates the real motor, which makesit difficult to ensure the accuracy of the model.

Therefore, it is necessary to provide a method which can adapt to thenonlinear control of the motor, so as to realize nonlinear control ofthe motor.

SUMMARY

In one independent aspect, a method for adaptive control of a motorgenerally includes a data acquisition step of acquiring currentparameters in an operation process of the motor at a current moment; adetermination step of determining a type of a region in which the motoroperates at the current moment according to the current parameters; atriggering step of triggering a corresponding motor model according tothe type of the region in which the motor operates at the currentmoment; and a controlling step of inputting the current parameters intothe corresponding motor model, generating control parameters for motoroperation according to the current parameters, and controlling theoperation of the motor according to the control parameters for motoroperation.

In some embodiments, the type of the region in which the motor operatesat the current moment includes a linear region and a nonlinear region,and

when it is determined that the type of the region in which the motoroperates at the current moment is the linear region, a linear model istriggered and the method proceeds to a linear control step; and

when it is determined that the type of the region in which the motoroperates at the current moment is the nonlinear region, a neural networkmodel is triggered and the method proceeds to a nonlinear control step.

In some embodiments, the linear control step includes inputting thecurrent parameters into the linear model to obtain the controlparameters for motor operation, and controlling the operation of themotor according to the control parameters for motor operation; and

the nonlinear control step includes inputting the current parametersinto the neural network model to obtain the control parameters for motoroperation, and controlling the operation of the motor according to thecontrol parameters for motor operation.

In some embodiments, the neural network model may include a time delayneural network model.

In some embodiments, training the time delay neural network model mayinclude acquiring individual parameters in the operation process of themotor from historical data, wherein the individual parameters are takenas parameters of input layer nodes of the time delay neural networkmodel; acquiring control parameters in the operation process of themotor from historical data, wherein the control parameters are taken asparameters of output layer nodes of the time delay neural network model;and determining the coefficient of each hidden layer node of the timedelay neural network model using a back propagation algorithm, andtraining the time delay neural network model.

In some embodiments, the current parameters in the operation process ofthe motor at the current moment may include a displacement of a motorvibrator at the current moment, and the determination step may includecomparing the displacement of the motor vibrator at the current momentwith a corresponding displacement threshold set by a system; anddetermining the type of the region in which the motor operates at thecurrent moment according to a result of the comparison between thedisplacement of the motor vibrator at the current moment and thedisplacement threshold.

In some embodiments, the data acquisition step may include predictingthe displacement of the motor vibrator at the current moment by adoptingthe linear model upon initial control by the system or when the motoroperates in the linear region; and predicting the displacement of themotor vibrator at the current moment by adopting the neural networkmodel when the motor operates in the nonlinear region.

In some embodiments, the linear model may adopt a second-order physicalmodel, a differential equation of which is as follows:

$\sum_{{b{(x)}i} = {{m\frac{d^{2}x}{dt^{2}}} + {R_{m}\frac{dx}{dt}} + {{k{(x)}}x} - {{L_{x}{(x)}}\frac{i^{2}}{2}}}}^{u = {{R_{e}i} + \frac{d{({{L{(x)}}i})}}{dt} + {{b{(x)}}\frac{dx}{dt}}}}.$

In another independent aspect, an apparatus for adaptive control of amotor generally includes a processor and a memory in communication withthe processor. The memory has a motor control program stored thereon.The motor control program is executable by the processor to perform themethod for adaptive control of a motor as described above.

In still another independent aspect, a computer-readable storage mediumis provided which has a motor control program stored thereon. The motorcontrol program is executable by a processor to perform the method foradaptive control of a motor as described above.

In summary, a double-model-switching based motor control method isdisclosed. When the motor operates in the linear region, the motor iscontrolled using the linear model. When the motor operates in thenonlinear region, the motor is controlled using the neural network modelin place of the linear model. Embodiments of the present disclosure canautomatically select a corresponding model for motor control based onthe region in which the motor operates. In comparison with theconventional motor control which uses a single nonlinear model, themotor control method disclosed herein can greatly improve thereliability of the control.

Independent features and/or independent advantages of this disclosuremay become apparent to those skilled in the art upon review of thedetailed description, claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions of the embodiments of thepresent disclosure more clearly, accompanying drawings used to describethe embodiments are briefly introduced below. It is evident that thedrawings in the following description are only concerned with someembodiments of the present disclosure. For those skilled in the art, ina case where no inventive effort is made, other drawings may be obtainedbased on these drawings.

FIG. 1 is a flowchart of a method for adaptive control of a motoraccording to one embodiment;

FIG. 2 is a flowchart of control of a linear model according to oneembodiment;

FIG. 3 is a flowchart of control of a neural network model according toone embodiment;

FIG. 4 is a diagram showing the logic parameter variation of the methodfor adaptive control of a motor according to one embodiment; and

FIG. 5 is a block diagram illustrating an apparatus for adaptive controlof a motor according to one embodiment.

DESCRIPTION OF THE EMBODIMENTS

The present disclosure will be described further below with reference tothe accompanying drawings and embodiments.

First Embodiment

As shown in FIGS. 1-3, the present disclosure provides a method foradaptive control of a motor, which controls the motor based on doublemodel switching. As shown in FIG. 4, it is a diagram showing the logicparameter variation in the motor control of the present disclosure. Aneural network model and a linear model are used as observers of themotor to calculate the control parameters of the motor according toindividual parameters in the operation process of the motor.

A neural network controller and a linear controller are used ascontrollers of the motor to receive the control parameters of the motoras calculated by the neural network model and the linear model, thusrealizing the control of the motor.

In the present disclosure, the motor is controlled based on double modelswitching. Therefore, during operation of the motor, a logic judgmentmodule shown in FIG. 4 is used to judge an operation region of the motorin real time, and automatically select whether the neural network modelor the linear model is to be used for calculation of the controlparameters of the motor according to a judgment result, therebyrealizing nonlinear or linear control of the motor.

That is, the linear model is triggered when the motor operates in alinear region, and at this time, individual parameters in the operationprocess of the motor are input into the linear model to obtain thecontrol parameters of the current motor, which are in turn inputted intothe linear controller to realize the linear control of the motor.

In the present disclosure, the linear control of the motor is realizedby adding the linear model, which improves the robustness of the system.The linear model adopts a traditional second-order physical model, adifferential equation of which is as follows:

$\begin{matrix}{\sum_{{b{(x)}i} = {{m\frac{d^{2}x}{dt^{2}}} + {R_{m}\frac{dx}{dt}} + {{k{(x)}}x} - {{L_{x}{(x)}}\frac{i^{2}}{2}}}}^{u = {{R_{e}i} + \frac{d{({{L{(x)}}i})}}{dt} + {{b{(x)}}\frac{dx}{dt}}}},} & (1)\end{matrix}$

where m is the mass, and k is a stiffness coefficient.

The neural network model is triggered when the motor operates in anonlinear region. At this time, individual parameters in the operationprocess of the motor are input into the neural network model to obtainthe current control parameters of the motor, and then the currentcontrol parameters of the motor are input into the neural networkcontroller to realize the nonlinear control of the motor.

The neural network model adopted in the present disclosure is obtainedthrough training. That is to say, the neural network model adopted inthe present disclosure adopts a time delay neural network model (TDNNmodel for short).

The specific training process of the TDNN model is as follows.

In an input layer of the TDNN model, each input node corresponds to thesampling point at each moment, that is, the input data of each inputnode is the parameters in the operation process of the motor as detectedat each moment, and the motor model is obtained through the calculationof multiple hidden layers.

In order to realize the nonlinear control of the motor by using theneural network model, it is also necessary to optimize the parameters ofeach layer. In the present disclosure, a back propagation algorithm anda softmax function (also called a normalized exponential function) areused as methods for gradient calculation to realize optimization of theparameters of each layer.

That is to say, the training process of the TDNN model is as follows.Individual parameters in the operation process of the motor are derivedfrom historical data and used as the input parameters of the neuralnetwork model, the control parameters for motor control are used as theoutput parameters of the neural network model, the back propagationalgorithm is adopted to determine a coefficient of each hidden layernode in the neural network model, i.e., determine individual parametersof the neural network model, and the neural network model is trained toobtain a neural network model.

The architecture and training process of the neural network model areknown in the art. In the present disclosure, the architecture andtraining of the neural network model is used to establish a model forexpressing a relationship between individual parameters in the operationprocess of the motor and the control parameters of the motor, such thatthe control parameters of the motor can be obtained according to theindividual parameters in the operation process of the motor at thecurrent moment, thereby realizing the real-time control of the motor.

The neural network model is triggered when the motor operates into thenonlinear region, and the parameters in the operation process of themotor as detected at the current moment are inputted into the neuralnetwork model to obtain the control parameters of the motor, and thenthe operation of the motor is controlled through the neural networkcontroller according to the calculated control parameters of the motor.

There are various methods for judging whether the motor operates in thenonlinear region.

In the present disclosure, a displacement estimation method is adoptedto judge whether the motor operates in a nonlinear region or not.

That is, firstly, a displacement threshold of the motor operating in thelinear region and a displacement threshold of the motor operating in thenonlinear region are evaluated by the system.

Then, in judging the region in which the motor operates, the real-timedisplacement of the motor at the current moment is compared with thecorresponding displacement threshold, to judge whether the motoroperates in the linear region or in the nonlinear region.

Furthermore, for the initial control, the system firstly designates acontrol mode, predicts the displacement of the motor using the linearmodel, and controls the operation of the motor by adopting a linearcontrol strategy. The actual displacement of the motor is obtained inreal time, and it is judged whether the region in which the motoroperates is a linear region. If yes, then the linear control strategy iscontinually used to control the motor operation; and if not, the neuralnetwork model is triggered, and the motor operation is controlled byadopting a nonlinear control strategy.

The work of the motor is controlled by adopting the neural networkcontrol strategy if the motor operates in the nonlinear region, and thestate of the motor is detected by using the neural network model. Thatis, the displacement of the motor is predicted in real time. When it isjudged that the motor operates in the linear region according to thepredicted displacement of the motor, the system automatically switchesto the linear control strategy to control the motor, and the linearmodel is then used in place of the neural network model for predictingthe state of the motor.

The operation of the motor is controlled by adopting the linear controlstrategy if the motor operates in the linear region, and the state ofthe motor is detected by using the neural network model. That is, thedisplacement for the motor is predicted in real time. When it is judgedthat the motor operates in the nonlinear region according to thepredicted displacement of the motor, the system automatically switchesto the neural network control strategy to control the motor, and theneural network model is used in place of the linear model for predictingthe state of the motor. The above control runs in cycle until the systemis stopped, thereby realizing reliable control of the motor.

In the present disclosure, double models are adopted to automaticallyadapt to the linear region and the nonlinear region for motor operation,so that each model can focus on the region where it is good at tocontrol the motor, which can better control the motor. In comparisonwith the conventional motor control which uses the single nonlinearmodel, the motor control method disclosed herein can greatly improve thereliability of the control.

As shown in FIG. 1, a method for adaptive control of a motor includesthe following steps.

At Step S100, current parameters in an operation process of the motor atthe current moment are acquired. For example, various detecting devicesare used to detect the current parameters in the operation process ofthe motor at the current moment. These parameters include, but are notlimited to, the displacement of a motor vibrator in the operationprocess of the motor.

At Step S200, a type of a region in which the motor operates at thecurrent moment is determined according to the current parameters.

Because of the different types of regions in which the motor operates,it is necessary to adopt different motor models to realize control ofthe motor in these regions.

At Step S300, a corresponding motor model is triggered according to thetype of the region in which the motor operates at the current moment.

At Step S400, the current parameters are inputted into the correspondingmotor model; control parameters for motor operation are generatedaccording to the current parameters; and the operation of the motor iscontrolled according to the control parameters for motor operation.

The motor model includes the linear model and the neural network model.Therefore, referring to FIGS. 2 and 3, the details of steps S300 andS400 are as follows.

At step S301, the linear model is triggered when it is determined thatthe type of the region in which the motor operates at the current momentis the linear region.

At step S401, the current parameters are inputted into the linear modelto obtain the control parameters for motor operation, and the operationof the motor is controlled according to the control parameters for motoroperation.

At step S302, the neural network model is triggered when it isdetermined that the type of the region in which the motor operates atthe current moment is the nonlinear region.

At step S402, the current parameters are inputted into the neuralnetwork model to obtain the control parameters for motor operation, andthe operation of the motor is controlled according to the controlparameters for motor operation.

Furthermore, in determining the type of the region in which the motoroperates, the displacement of the motor vibrator is compared with thecorresponding displacement threshold set by the system, and the type ofthe region in which the motor operates at the current moment isdetermined according to the comparison result.

Furthermore, because the type of the region in which the motor operatesis unknown at the beginning of the system, the displacement of the motorvibrator at the current moment is predicted using the linear model uponthe initial control by the system.

Second Embodiment

The present disclosure provides an apparatus for adaptive control of amotor. FIG. 5 is a schematic diagram illustrating an internal structureof an apparatus for adaptive control of a motor according to anembodiment of the present disclosure.

In this embodiment, the apparatus for adaptive control of a motor may bea PC (Personal Computer), or a terminal device such as a smart phone, atablet computer, and a portable computer. The apparatus for adaptivecontrol of a motor at least includes a processor 12, a communication bus13, a network interface 14, and a memory 11.

The memory 11 at least includes one type of readable storage medium,which includes a flash memory, a hard disk, a multimedia card, acard-type memory (e.g., a SD or DX memory, etc.), a magnetic memory, amagnetic disk, an optical disk, etc. In some embodiments, the memory 11may be an internal storage unit of the apparatus for adaptive control ofa motor, such as a hard disk of the apparatus for adaptive control of amotor. In some other embodiments, the memory 11 may also be an externalstorage device of the apparatus for adaptive control of a motor, such asa plug-in hard disk, a smart media card (SMC), a secure digital (SD)card, a flash card, etc. provided on the apparatus for adaptive controlof a motor. Further, the memory 11 may also include both the internalstorage unit and the external storage device of the apparatus foradaptive control of a motor. The memory 11 can be used not only forstoring application software and various data installed in the apparatusfor adaptive control of a motor, such as codes of the motor controlprogram, but also for temporarily storing data that has been outputtedor will be outputted.

In some embodiments, the processor 12 can be a central processing unit(CPU), a controller, a microcontroller, a microprocessor or other dataprocessing chips, and is used for running program codes or processingdata stored in the memory 11, such as executing the motor controlprogram, etc.

The communication bus 13 is used for realizing communication betweenthese components.

The network interface 14 may optionally include a standard wiredinterface or a wireless interface (e.g., a WI-FI interface), and isusually used for establishing a communication connection between theapparatus for adaptive control of a motor and other electronic devices.

Optionally, the apparatus for adaptive control of a motor may alsoinclude a user interface. The user interface may include a display andan input unit such as a keyboard, and the optional user interface mayalso include a standard wired interface and a wireless interface.Optionally, in some embodiments, the display may be an LED display, aliquid crystal display, a touch-sensitive liquid crystal display, anOLED (organic light-emitting diode) touch device, and the like. In thisdisclosure, the display can also be appropriately termed as a displayscreen or a display unit, which is used for displaying informationprocessed in the apparatus for adaptive control of a motor, anddisplaying a visual user interface.

FIG. 5 only shows the components 11-14 and a motor control program ofthe apparatus for adaptive control of a motor. It should be understoodby those skilled in the art that, the structures shown in FIG. 5 do notconstitute a limit to the apparatus for adaptive control of a motor, andthe apparatus for adaptive control of a motor may include fewer or morecomponents than those shown, or a combination of some components, orhave different component arrangements.

In the embodiment of the apparatus for adaptive control of a motor asshown in FIG. 5, a motor control program is stored in the memory 11.When the processor 12 executes the motor control program stored in thememory 11, the following steps are implemented:

data acquisition step S100: acquiring current parameters in an operationprocess of the motor at a current moment;

determination step S200: determining the type of a region in which themotor operates at the current moment according to the currentparameters;

triggering step S300: triggering a corresponding motor model accordingto the type of the region in which the motor operates at the currentmoment; and

controlling step S400: inputting the current parameters into thecorresponding motor model; generating control parameters for motoroperation according to the current parameters; and controlling theoperation of the motor according to the control parameters for motoroperation.

Further, the type of the region in which the motor operates at thecurrent moment includes the linear region and the nonlinear region, andthe determination step S300 includes:

step S301: triggering the linear model when it is determined that thetype of the region in which the motor operates at the current moment isthe linear region, and proceeding to a linear control step S401; and

step S302: triggering the neural network model when it is determinedthat the type of the region in which the motor operates at the currentmoment is the nonlinear region, and proceeding to a nonlinear controlstep S402.

Further, in the linear control step S401, the current parameters areinputted into the neural network model to obtain the control parametersfor motor operation, and the operation of the motor is controlledaccording to the control parameters for motor operation.

In the nonlinear control step S402, the current parameters are inputtedinto the neural network model to obtain the control parameters for motoroperation, and the operation of the motor is controlled according to thecontrol parameters for motor operation.

In some embodiments, the neural network model is a time delay neuralnetwork model.

In some embodiments, the step of training the time delay neural networkmodel includes:

acquiring individual parameters in the operation process of the motorfrom historical data, wherein the individual parameters are taken as theparameters of input layer nodes of the time delay neural network model;

acquiring control parameters in the operation process of the motor fromhistorical data, wherein the control parameters are taken as theparameters of output layer nodes of the time delay neural network model;and

determining the coefficient of each hidden layer node of the time delayneural network model using a back propagation algorithm, and trainingthe time delay neural network model.

Further, the current parameters in the operation process of the motor atthe current moment include the displacement of a motor vibrator at thecurrent moment, and the determination step S300 includes:

comparing the displacement of the motor vibrator at the current momentwith a corresponding displacement threshold set by a system; and

determining the type of a region in which the motor operates at thecurrent moment according to a comparison result.

Further, the data acquisition step S100 includes:

predicting the displacement of the motor vibrator at the current momentby adopting the linear model upon initial control by the system or whenthe motor operates in the linear region; and

predicting the displacement of the motor vibrator at the current momentby adopting the neural network model when the motor operates in thenonlinear region.

Further, the linear model adopts a second-order physical model.

Third Embodiment

Furthermore, one embodiment of the present disclosure also provides astorage medium, which is a computer-readable storage medium on which amotor control program is stored, and the motor control program can beexecuted by one or more processors to realize the following operations.

At Step S100, current parameters in an operation process of the motor atthe current moment are acquired. For example, various detecting devicesare used to obtain the current parameters in the operation process ofthe motor at the current moment. These parameters include, but are notlimited to, the displacement of the motor vibrator in the operationprocess of the motor.

At Step S200, the type of a region in which the motor operates at thecurrent moment is determined according to the current parameters.

Because of the different types of regions in which the motor operates,it is necessary to adopt different motor models to realize control ofthe motor in these regions.

At Step S300, a corresponding motor model is triggered according to thetype of the region in which the motor operates at the current moment.

At Step S400: the current parameters are inputted into the correspondingmotor model; control parameters for motor operation are generatedaccording to the current parameters; and the operation of the motor iscontrolled according to the control parameters for motor operation.

The implementation of the storage medium of the present disclosure isbasically the same as the above embodiments of the method and apparatusfor adaptive control of the motor, and will not be described here infurther detail.

Although the disclosure is described with reference to one or moreembodiments, it will be apparent to those skilled in the art thatvarious modifications and variations can be made to the disclosedstructure and method without departing from the scope or spirit of thedisclosure. In view of the foregoing, it is intended that the presentdisclosure cover modifications and variations of this invention providedthey fall within the scope of the following claims and theirequivalents.

What is claimed is:
 1. A method for adaptive control of a motor,comprising: a data acquisition step of acquiring current parameters inan operation process of the motor at a current moment; a determinationstep of determining a type of a region in which the motor operates atthe current moment according to the current parameters; a triggeringstep of triggering a corresponding motor model according to the type ofthe region in which the motor operates at the current moment; and acontrolling step of inputting the current parameters into thecorresponding motor model, generating control parameters for motoroperation according to the current parameters, and controlling theoperation of the motor according to the control parameters for motoroperation.
 2. The method for adaptive control of a motor according toclaim 1, wherein the type of the region in which the motor operates atthe current moment comprises a linear region and a nonlinear region, andwhen it is determined that the type of the region in which the motoroperates at the current moment is the linear region, a linear model istriggered and the method proceeds to a linear control step; and when itis determined that the type of the region in which the motor operates atthe current moment is the nonlinear region, a neural network model istriggered and the method proceeds to a nonlinear control step.
 3. Themethod for adaptive control of a motor according to claim 2, wherein thelinear control step comprises inputting the current parameters into thelinear model to obtain the control parameters for motor operation, andcontrolling the operation of the motor according to the controlparameters for motor operation; and the nonlinear control step comprisesinputting the current parameters into the neural network model to obtainthe control parameters for motor operation, and controlling theoperation of the motor according to the control parameters for motoroperation.
 4. The method for adaptive control of a motor according toclaim 3, wherein the neural network model comprises a time delay neuralnetwork model.
 5. The method for adaptive control of a motor accordingto claim 4, wherein the method comprises a step of training the timedelay neural network model, the step of training comprising: acquiringindividual parameters in the operation process of the motor fromhistorical data, wherein the individual parameters are taken asparameters of input layer nodes of the time delay neural network model;acquiring control parameters in the operation process of the motor fromhistorical data, wherein the control parameters are taken as parametersof output layer nodes of the time delay neural network model; anddetermining the coefficient of each hidden layer node of the time delayneural network model using a back propagation algorithm, and trainingthe time delay neural network model.
 6. The method for adaptive controlof a motor according to claim 3, wherein the current parameters in theoperation process of the motor at the current moment comprise thedisplacement of a motor vibrator at the current moment, and thedetermination step comprises: comparing the displacement of the motorvibrator at the current moment with a corresponding displacementthreshold set by a system; and determining the type of the region inwhich the motor operates at the current moment according to a result ofthe comparison between the displacement of the motor vibrator at thecurrent moment and the displacement threshold.
 7. The method foradaptive control of a motor according to claim 6, wherein the dataacquisition step comprises: predicting the displacement of the motorvibrator at the current moment by adopting the linear model upon initialcontrol by the system or when the motor operates in the linear region;and predicting the displacement of the motor vibrator at the currentmoment by adopting the neural network model when the motor operates inthe nonlinear region.
 8. The method for adaptive control of a motoraccording to claim 7, wherein the linear model adopts a second-orderphysical model, a differential equation of which is as follows:$\sum_{{b{(x)}i} = {{m\frac{d^{2}x}{dt^{2}}} + {R_{m}\frac{dx}{dt}} + {{k{(x)}}x} - {{L_{x}{(x)}}\frac{i^{2}}{2}}}}^{u = {{R_{e}i} + \frac{d{({{L{(x)}}i})}}{dt} + {{b{(x)}}\frac{dx}{dt}}}},$where m is a mass, and k is a stiffness coefficient.
 9. An apparatus foradaptive control of a motor, comprising a processor and a memory incommunication with the processor, the memory having a motor controlprogram stored thereon, the motor control program being executable bythe processor to perform a method for adaptive control of the motor, themethod comprising: a data acquisition step of acquiring currentparameters in an operation process of the motor at a current moment; adetermination step of determining a type of a region in which the motoroperates at the current moment according to the current parameters; atriggering step of triggering a corresponding motor model according tothe type of the region in which the motor operates at the currentmoment; and a controlling step of inputting the current parameters intothe corresponding motor model, generating control parameters for motoroperation according to the current parameters, and controlling theoperation of the motor according to the control parameters for motoroperation.
 10. The apparatus according to claim 9, wherein the type ofthe region in which the motor operates at the current moment comprises alinear region and a nonlinear region, when it is determined that thetype of the region in which the motor operates at the current moment isthe linear region, a linear model is triggered and the method proceedsto a linear control step; and when it is determined that the type of theregion in which the motor operates at the current moment is thenonlinear region, a neural network model is triggered and the methodproceeds to a nonlinear control step.
 11. The apparatus according toclaim 10, wherein the linear control step comprises inputting thecurrent parameters into the linear model to obtain the controlparameters for motor operation, and controlling the operation of themotor according to the control parameters for motor operation; and thenonlinear control step comprises inputting the current parameters intothe neural network model to obtain the control parameters for motoroperation, and controlling the operation of the motor according to thecontrol parameters for motor operation.
 12. The apparatus according toclaim 11, wherein the neural network model comprises a time delay neuralnetwork model, and the method comprises a step of training the timedelay neural network model, the step of training comprising: acquiringindividual parameters in the operation process of the motor fromhistorical data, wherein the individual parameters are taken asparameters of input layer nodes of the time delay neural network model;acquiring control parameters in the operation process of the motor fromhistorical data, wherein the control parameters are taken as parametersof output layer nodes of the time delay neural network model; anddetermining the coefficient of each hidden layer node of the time delayneural network model using a back propagation algorithm, and trainingthe time delay neural network model.
 13. The apparatus according toclaim 11, wherein the current parameters in the operation process of themotor at the current moment comprise the displacement of a motorvibrator at the current moment, and the determination step comprises:comparing the displacement of the motor vibrator at the current momentwith a corresponding displacement threshold set by a system; anddetermining the type of the region in which the motor operates at thecurrent moment according to a result of the comparison between thedisplacement of the motor vibrator at the current moment and thedisplacement threshold.
 14. The apparatus according to claim 13, whereinthe data acquisition step comprises: predicting the displacement of themotor vibrator at the current moment by adopting the linear model uponinitial control by the system or when the motor operates in the linearregion; and predicting the displacement of the motor vibrator at thecurrent moment by adopting the neural network model when the motoroperates in the nonlinear region.
 15. The apparatus according to claim14, wherein the linear model adopts a second-order physical model, adifferential equation of which is as follows:$\sum_{{b{(x)}i} = {{m\frac{d^{2}x}{dt^{2}}} + {R_{m}\frac{dx}{dt}} + {{k{(x)}}x} - {{L_{x}{(x)}}\frac{i^{2}}{2}}}}^{u = {{R_{e}i} + \frac{d{({{L{(x)}}i})}}{dt} + {{b{(x)}}\frac{dx}{dt}}}}$where m is a mass, and k is a stiffness coefficient.
 16. Acomputer-readable storage medium having a motor control program storedthereon, the motor control program being executable by a processor toexecute a method for adaptive control of a motor, the method comprising:a data acquisition step of acquiring current parameters in an operationprocess of the motor at a current moment; a determination step ofdetermining a type of a region in which the motor operates at thecurrent moment according to the current parameters; a triggering step oftriggering a corresponding motor model according to the type of theregion in which the motor operates at the current moment; and acontrolling step of inputting the current parameters into thecorresponding motor model, generating control parameters for motoroperation according to the current parameters, and controlling theoperation of the motor according to the control parameters for motoroperation.
 17. The computer-readable storage medium according to claim16, wherein the type of the region in which the motor operates at thecurrent moment comprises a linear region and a nonlinear region, when itis determined that the type of the region in which the motor operates atthe current moment is the linear region, a linear model is triggered andthe method proceeds to a linear control step; and when it is determinedthat the type of the region in which the motor operates at the currentmoment is the nonlinear region, a neural network model is triggered andthe method proceeds to a nonlinear control step. wherein the linearcontrol step comprises inputting the current parameters into the linearmodel to obtain the control parameters for motor operation, andcontrolling the operation of the motor according to the controlparameters for motor operation; and the nonlinear control step comprisesinputting the current parameters into the neural network model to obtainthe control parameters for motor operation, and controlling theoperation of the motor according to the control parameters for motoroperation.
 18. The computer-readable storage medium according to claim17, wherein the current parameters in the operation process of the motorat the current moment comprise the displacement of a motor vibrator atthe current moment, and the determination step comprises: comparing thedisplacement of the motor vibrator at the current moment with acorresponding displacement threshold set by a system; and determiningthe type of the region in which the motor operates at the current momentaccording to a result of the comparison between the displacement of themotor vibrator at the current moment and the displacement threshold. 19.The computer-readable storage medium according to claim 18, wherein thedata acquisition step comprises: predicting the displacement of themotor vibrator at the current moment by adopting the linear model uponinitial control by the system or when the motor operates in the linearregion; and predicting the displacement of the motor vibrator at thecurrent moment by adopting the neural network model when the motoroperates in the nonlinear region.
 20. The computer-readable storagemedium according to claim 19, wherein the linear model adopts asecond-order physical model, a differential equation of which is asfollows:$\sum_{{b{(x)}i} = {{m\frac{d^{2}x}{dt^{2}}} + {R_{m}\frac{dx}{dt}} + {{k{(x)}}x} - {{L_{x}{(x)}}\frac{i^{2}}{2}}}}^{u = {{R_{e}i} + \frac{d{({{L{(x)}}i})}}{dt} + {{b{(x)}}\frac{dx}{dt}}}}.$where m is a mass, and k is a stiffness coefficient.